Electricity/Solar Update

A look at the latest Jedlovec House eletricity usage and solar production from PPL and Enphase.

Load packages

library(tidyverse)
library(lubridate)
library(hms)
library(readxl)
library(onlineBcp)

Load PPL data

col_datatypes <- c('numeric','numeric','date','text',rep('numeric',99))

hourly1 <- read_excel("Hourly Usage 220326 to 220624.xlsx", col_types = col_datatypes)

Warning: Expecting numeric in A277 / R277C1: got 'The information contained in this file is intended for the confidential use by the customer and third parties authorized by the customer to receive the information. Any unauthorized use is prohibited.'

hourly2 <- read_excel("Hourly Usage 220625 to 230623.xlsx", col_types = col_datatypes)

Warning: Expecting numeric in A1096 / R1096C1: got 'The information contained in this file is intended for the confidential use by the customer and third parties authorized by the customer to receive the information. Any unauthorized use is prohibited.'

hourly3 <- read_excel("PPL 230624 to 240509.xlsx", col_types = col_datatypes)

Warning: Expecting numeric in A967 / R967C1: got 'The information contained in this file is intended for the confidential use by the customer and third parties authorized by the customer to receive the information. Any unauthorized use is prohibited.'

#hourly1
#hourly2
#hourly3

ppl_15mins <- bind_rows(hourly1,list(hourly2,hourly3))

ppl_15mins %>% arrange(desc(Date), `Read Type`)

NA
NA

Transform PPL Data

hourly_ppl_pivot <- ppl_15mins %>% 
  rename(date = Date) %>% 
  pivot_longer(!c("Account Number", "Meter Number", date, "Read Type", Min, Max, Total), names_to = "time", values_to = "kWh") 

rename(ppl_15mins, date = Date)

hourly_ppl_pivot <- hourly_ppl_pivot %>% 
  mutate(time = parse_time(time, '%H:%M %p'), month = month(date, label=TRUE), year = year(date), yday = yday(date), wday = wday(date, label=TRUE))

(hourly_ppl_net <- hourly_ppl_pivot %>% 
  filter(`Read Type` == "kWh Net"))

Load Enphase data

import <- read_csv("enphase_history_230701.csv")

Rows: 42432 Columns: 2── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Date/Time
dbl (1): Energy Produced (Wh)
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

update <- read_csv("hourly_generation_230625_240510.csv")

Rows: 30816 Columns: 2── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Date/Time
dbl (1): Energy Produced (Wh)
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

update <- update %>% 
  mutate(`Date/Time` = as.POSIXct(`Date/Time`,format="%m/%d/%Y %H:%M",tz=Sys.timezone())) %>% 
  filter(`Date/Time` >= as.POSIXct('07/01/2023 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) )

import <- import %>% 
  mutate(`Date/Time` = as.POSIXct(`Date/Time`,format="%m/%d/%Y %H:%M",tz=Sys.timezone())) 

full_hist <- rbind(import,update)

full_hist %>% arrange(desc(`Date/Time`))

(hourly_production <- full_hist %>% 
  rename(datetime = `Date/Time`, energy_produced_Wh = `Energy Produced (Wh)`) %>% 
  mutate(datetime = as.POSIXct(datetime,format="%m/%d/%Y %H:%M",tz=Sys.timezone())) %>% 
  mutate(date = date(datetime), time = as.hms(format(datetime, format = "%H:%M:%S")), month = month(datetime, label=TRUE), year = year(datetime), day = day(datetime), yday = yday(datetime), monthday = format(datetime, "%m-%d"), wday = wday(datetime, label=TRUE), equinox_day = (yday + 10) %% 365, equinox_group = floor((equinox_day+15)/30)*30)
)

NA

Spot-check Enphase Should see lifetime production by day and by hour

ggplot(hourly_production, aes(datetime, energy_produced_Wh)) +
  geom_point()

ggplot(hourly_production, aes(time, energy_produced_Wh)) +
  theme(axis.text.x = element_text(angle = 90)) +
  geom_point()

Net + Produced = Consumed

# hourly_ppl_net <- hourly_ppl_net %>% mutate(date = as_date(date))

#hourly_ppl_net %>% arrange(desc(date))
#hourly_production %>% arrange(desc(date))

(hourly_electricity <- hourly_ppl_net %>% 
    inner_join(hourly_production, by = join_by(date,time))  %>% 
    mutate(consumed_kWh = kWh + energy_produced_Wh/1000, produced_kWh = energy_produced_Wh/1000)  %>% 
    rename(net_kWh = kWh) %>% 
    select(datetime, date, time, net_kWh, produced_kWh, consumed_kWh) %>%
    arrange(date))

NA

Calculate production for first year of solar panels, second year, etc.

Interesting! Produced kWh went down by 5%, but consumed kWh went down by 10% despite the fact that we got an electric car! Let’s explore that further.

daily_electricity <- hourly_electricity %>% 
  group_by(solar_year, yday) %>% 
  summarize(net_kWh = sum(net_kWh), produced_kWh = sum(produced_kWh), consumed_kWh = sum(consumed_kWh))

`summarise()` has grouped output by 'solar_year'. You can override using the `.groups` argument.

ggplot(daily_electricity, aes(yday, consumed_kWh, group=solar_year, color=solar_year)) + 
  geom_point() +
  geom_smooth(span=0.3) +
  scale_x_continuous(breaks = c(1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335), labels = month.abb) +
  theme(axis.text.x = element_text(angle = 90)) 

Daily totals

daily_electricity <- hourly_electricity %>% 
  mutate(solar_day = interval(as_date(as.POSIXct('04/15/2022',format="%m/%d/%Y",tz=Sys.timezone())),as_date(date)) / days(1) 
         ) %>% 
  group_by(date, solar_day) %>% 
  summarize(net_kWh = sum(net_kWh), produced_kWh = sum(produced_kWh), consumed_kWh = sum(consumed_kWh))

`summarise()` has grouped output by 'date'. You can override using the `.groups` argument.

ggplot(daily_electricity, aes(date, consumed_kWh)) + 
  geom_point() +
  #scale_x_continuous(breaks = c(1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335), labels = month.abb) +
  theme(axis.text.x = element_text(angle = 90)) 

NA
NA

Look how our electricity consumption got much less predictable, more uneven, after we purchased an EV and installed a level 2 charger in late January/early February 2023!

Also, a couple of outlier high points in Dec 2022 before we got the EV… hmm.

We probably need to filter out the EV charging and compare that separately.

Average consumption based on time of day

#hourly_electricity %>% summarize(min_date = min(date), max_date = max(date))

electricity_by_time <- hourly_electricity %>% 
  filter(solar_year == 1 | solar_year == 2) %>% 
  #filter(date <= as.POSIXct('12/31/2022 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ) %>% 
  group_by(time, solar_year) %>% 
  summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>% 
  arrange(time)

`summarise()` has grouped output by 'time'. You can override using the `.groups` argument.

electricity_by_time

ggplot(electricity_by_time, aes(x=time, y=consumed_kWh, group=solar_year, color=solar_year)) +
  geom_point() 

  #labs(colour="",x="Time of Day",y="Electricity Consumption (kWh)")+
  #scale_color_manual(values = c("red","black","green")) +
  #ggtitle("Home Electricity in 15-Minute Intervals (Since April 15, 2022)")

Wow, look how much less was used during the day in year 2!!! Like a 50% reduction!

Is this due to milder weather?

Let’s look at it by month:

#electricity_by_month <- 
hourly_electricity %>% 
  mutate(month = month(date)) %>% 
  group_by(solar_year, month) %>% 
  summarize(consumed_kWh = mean(consumed_kWh)) %>% 
  arrange(month, solar_year) %>% 
  pivot_wider(names_from = month, values_from = consumed_kWh) %>% 
  arrange(solar_year)

`summarise()` has grouped output by 'solar_year'. You can override using the `.groups` argument.

#Look at limited window during day (no EV charging)
 
# hourly_electricity %>% 
#   filter(time == '12:00:00') %>% 
#   #filter(time > as.POSIXct('04:00:00',format="%H:%M:$s",tz=Sys.timezone()) ) %>%  #& time < as.POSIXct('18:00',format="%H:%M",tz=Sys.timezone())) %>% 
#   mutate(month = month(date)) %>% 
#   group_by(solar_year, month) %>% 
#   summarize(consumed_kWh = mean(consumed_kWh)) %>% 
#   arrange(month, solar_year) %>% 
#   pivot_wider(names_from = month, values_from = consumed_kWh) %>% 
#   arrange(solar_year)

Look at specific months

electricity_by_month_time <- hourly_electricity %>% 
  mutate(month = as_factor(month(date))) %>% 
  filter(solar_year == 1 | solar_year == 2) %>% 
  #Sample month
  filter(month == 6) %>% 
  group_by(solar_year, month, time) %>% 
  summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>% 
  arrange(solar_year, month, time)

`summarise()` has grouped output by 'solar_year', 'month'. You can override using the `.groups` argument.

ggplot(electricity_by_month_time, aes(time, consumed_kWh, group=solar_year, color=solar_year)) +
  #facet_grid(rows = vars(month)) +
  geom_point(size=0.5) +
  ggtitle("Home Electricity in 15-Minute Intervals") +
  ylab("Electricity Consumption (kWh)")

Example day with EV charging

sample_day <- hourly_electricity %>% 
  filter(solar_year == 1 | solar_year == 2) %>% 
  filter(yday == 161) %>% 
  group_by(date, solar_year, yday, time) %>% 
  summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>% 
  arrange(date, solar_year, yday, time)

`summarise()` has grouped output by 'date', 'solar_year', 'yday'. You can override using the `.groups` argument.

ggplot(sample_day, aes(time, consumed_kWh, group=solar_year, color=solar_year)) +
  #facet_grid(rows = vars(month)) +
  geom_point(size=0.5) +
  ggtitle("Home Electricity in 15-Minute Intervals") +
  ylab("Electricity Consumption (kWh)")

Ok, let’s detect and remove EV charging sessions.

Bayesian change point example

# library(onlineBcp)
x <- c(rnorm(10, 2.6, 0.2), rnorm(1, 2.6/2, 0.2) + rnorm(1, .2, .15), rnorm(86, .2, .15))
bcp <- online_cp(x)
summary(bcp)

[1] "Change points"

[1] "Segments"
     begin end      mean        SD  LL of CI UL of CI
[1,]     1  11 2.4159210 0.2461319 2.2938538 2.537988
[2,]    12  97 0.2128337 0.1483191 0.1865265 0.239141

bcp

$x
 [1]  2.3720905715  2.4691440621  2.6071250448  2.5352472423  2.2877005354  2.3857597432  2.7419924694  2.3295656691
 [9]  2.6664633812  2.3582885866  1.8217537652  0.0930585696  0.3230388724  0.0197739677  0.2570762359  0.1881634498
[17]  0.0792020254  0.1498961292  0.3642266363  0.3817626425  0.1885297288  0.3544041698  0.2879920055  0.0953605082
[25]  0.0446978082  0.0004123957  0.1959692210  0.0693755586  0.4736216346  0.1488568573  0.2188121995  0.0812417378
[33]  0.3080128352  0.4839328145  0.3567281316  0.0095019304  0.4530073963  0.3672416208  0.3294527060  0.1982238893
[41]  0.2996424154  0.1570348120  0.2920901580  0.2199320912  0.0822511010  0.4977626932  0.2455526621  0.4334779288
[49]  0.2376445401  0.2338382842 -0.0402372143  0.2225591660  0.1183037401  0.3194898662  0.1986949765  0.1917810490
[57]  0.0807082047  0.2332076344 -0.1524857206  0.0582340793  0.0665107477  0.0714005187 -0.0557819132  0.2851434290
[65]  0.3313943646  0.2103237245 -0.1036259056  0.1887882159  0.2929783093  0.0780226903 -0.1688743454  0.4017997097
[73]  0.3595800053  0.1888407383  0.1824374153  0.1784951176  0.1609317339  0.2098454959  0.3643482946  0.3487322792
[81]  0.2841377275  0.2780164560  0.0203063376  0.0949771645  0.2602946798  0.3359891574  0.1761905070  0.5173409818
[89]  0.1462872854  0.4356854056  0.1592010947  0.3728770785  0.2836787830  0.1263039884  0.4396970634  0.0967142059
[97]  0.2336566264

$max_p
           [,1] [,2] [,3]
 [1,] 0.9577733    0    0
 [2,] 0.9383008    0    1
 [3,] 0.9289778    0    2
 [4,] 0.9231639    0    3
 [5,] 0.9241976    0    4
 [6,] 0.9287806    0    5
 [7,] 0.9299975    0    6
 [8,] 0.9358318    0    7
 [9,] 0.9381648    0    8
[10,] 0.9254240    0    9
[11,] 0.4559258    0   10
[12,] 0.6111704   11    0
[13,] 0.8090391   11    1
[14,] 0.7996688   11    2
[15,] 0.7973283   11    3
[16,] 0.7991989   11    4
[17,] 0.8056386   11    5
[18,] 0.8089248   11    6
[19,] 0.8134703   11    7
[20,] 0.8252870   11    8
[21,] 0.8317779   11    9
[22,] 0.8411875   11   10
[23,] 0.8487528   11   11
[24,] 0.8538773   11   12
[25,] 0.8569608   11   13
[26,] 0.8680025   11   14
[27,] 0.8731902   11   15
[28,] 0.8663527   11   16
[29,] 0.8785399   11   17
[30,] 0.8858196   11   18
[31,] 0.8890118   11   19
[32,] 0.8923711   11   20
[33,] 0.8820230   11   21
[34,] 0.8895051   11   22
[35,] 0.8903923   11   23
[36,] 0.8879874   11   24
[37,] 0.8942148   11   25
[38,] 0.8993633   11   26
[39,] 0.9044254   11   27
[40,] 0.9066541   11   28
[41,] 0.9088015   11   29
[42,] 0.9107461   11   30
[43,] 0.9130828   11   31
[44,] 0.9113711   11   32
[45,] 0.9002217   11   33
[46,] 0.9116611   11   34
[47,] 0.9077451   11   35
[48,] 0.9147518   11   36
[49,] 0.9175931   11   37
[50,] 0.9030689   11   38
[51,] 0.9158742   11   39
[52,] 0.9176531   11   40
[53,] 0.9198118   11   41
[54,] 0.9222592   11   42
[55,] 0.9235917   11   43
[56,] 0.9208759   11   44
[57,] 0.9248626   11   45
[58,] 0.8835135   11   46
[59,] 0.9079202   11   47
[60,] 0.9164723   11   48
[61,] 0.9203609   11   49
[62,] 0.9085255   11   50
[63,] 0.9224407   11   51
[64,] 0.9244629   11   52
[65,] 0.9285093   11   53
[66,] 0.9042838   11   54
[67,] 0.9240923   11   55
[68,] 0.9277552   11   56
[69,] 0.9275764   11   57
[70,] 0.8848931   11   58
[71,] 0.9081096   11   59
[72,] 0.9189911   11   60
[73,] 0.9283150   11   61
[74,] 0.9312630   11   62
[75,] 0.9325588   11   63
[76,] 0.9331760   11   64
[77,] 0.9340624   11   65
[78,] 0.9292055   11   66
[79,] 0.9290267   11   67
[80,] 0.9322733   11   68
[81,] 0.9335335   11   69
[82,] 0.9274462   11   70
[83,] 0.9314169   11   71
[84,] 0.9346385   11   72
[85,] 0.9326805   11   73
[86,] 0.9357910   11   74
[87,] 0.9075830   11   75
[88,] 0.9290786   11   76
[89,] 0.9214965   11   77
[90,] 0.9325243   11   78
[91,] 0.9300501   11   79
[92,] 0.9343632   11   80
[93,] 0.9350886   11   81
[94,] 0.9243810   11   82
[95,] 0.9313401   11   83
[96,] 0.9362280   11   84
[97,] 0.0000000    0    0

$parameters
theta alpha  beta th_cp 
  0.9   1.0   1.0   0.5 

$series_length
[1] 97

$result
NULL

attr(,"class")
[1] "BayesCP"

Test on electricity consumption data

# library(onlineBcp)

#Select a sample to test
sample <- hourly_electricity %>% 
  mutate(solar_day = interval(as_date(as.POSIXct('04/15/2022',format="%m/%d/%Y",tz=Sys.timezone())),as_date(date)) / days(1) ) %>%
  filter(solar_year == 2) %>% 
  filter(yday < 38 & yday > 35)

ggplot(sample, aes(datetime, consumed_kWh, group=solar_year, color=solar_year)) +
  #facet_grid(rows = vars(month)) +
  geom_point(size=0.5) +
  ggtitle("Home Electricity in 15-Minute Intervals") +
  ylab("Electricity Consumption (kWh)")

bcp model comparisons: https://lindeloev.github.io/mcp/articles/packages.html

min_time <- pull(sample %>% summarize(min_time = min(datetime)))

sample <- sample %>% mutate(time_x = interval(min_time,datetime)/minutes(15)+1)

#Filter data to feed to model
x <- sample %>%  
  ungroup() %>% 
  select(consumed_kWh)

x <- x$consumed_kWh

#Run online Bayesian Change Point model 
#"Online" means in progress, not retroactive
bcp <- online_cp(x)

plot(summary(bcp))

[1] "Change points"

[1] "Segments"
     begin end      mean        SD  LL of CI  UL of CI
[1,]     1  22 0.3472727 0.1636542 0.2898818 0.4046636
[2,]    23  71 0.3963061 0.6793674 0.2366690 0.5559433
[3,]    72  86 2.6233333 0.2818983 2.5036113 2.7430554
[4,]    87 118 0.3384375 0.1960597 0.2814289 0.3954461
[5,]   119 131 1.6671538 0.6909474 1.3519434 1.9823643
[6,]   132 192 0.4151148 0.2525212 0.3619333 0.4682962

Not too bad! It picks up the EV charging sessions, but it also picks up some other, presumably HVAC-related consumption changes. It looks like I should be able to weed these out easily.

#Select a sample to test
sample <- hourly_electricity %>% 
  #filter(solar_year == 2) %>% 
  mutate(solar_day = interval(as_date(as.POSIXct('04/15/2022',format="%m/%d/%Y",tz=Sys.timezone())),as_date(date)) / days(1) ) 

ggplot(sample, aes(datetime, consumed_kWh, group=solar_year, color=solar_year)) +
  #facet_grid(rows = vars(month)) +
  geom_point(size=0.5) +
  ggtitle("Home Electricity in 15-Minute Intervals") +
  ylab("Electricity Consumption (kWh)")

You can pick out our family vacation in August, and you can also see a period of high electricity usage in February, presumably related to cold weather. - Verify with weather data?

min_time <- pull(sample %>% summarize(min_time = min(datetime)))

sample <- sample %>% 
  mutate(time_x = interval(min_time,datetime)/minutes(15)+1)

#Filter data to feed to model
x <- sample %>%  
  ungroup() %>% 
  select(consumed_kWh)

x <- x$consumed_kWh

#Run online Bayesian Change Point model 
#"Online" means in progress, not retroactive
bcp <- online_cp(x)

summary(bcp)

[1] "Change points"

[1] "Segments"
       begin   end       mean         SD   LL of CI   UL of CI
  [1,]     1    68 0.11851471 0.05251481 0.10803969 0.12898972
  [2,]    69    89 0.70085714 0.55165300 0.50284875 0.89886553
  [3,]    90   233 0.14222917 0.09243684 0.12955874 0.15489959
  [4,]   234   240 0.91714286 0.55476224 0.57224926 1.26203645
  [5,]   241   408 0.37915476 0.27470555 0.34429375 0.41401578
  [6,]   409   410 1.73000000 0.66468037 0.95691880 2.50308120
  [7,]   411   524 0.45407895 0.32414913 0.40414226 0.50401563
  [8,]   525   526 1.84250000 0.59609102 1.14919420 2.53580580
  [9,]   527   599 0.17747945 0.06771880 0.16444253 0.19051638
 [10,]   600   602 1.19966667 0.60550007 0.62464948 1.77468385
 [11,]   603   696 0.22370213 0.15978499 0.19659401 0.25081024
 [12,]   697   724 0.26989286 0.26216920 0.18839805 0.35138766
 [13,]   725   792 0.21539706 0.23737175 0.16804904 0.26274508
 [14,]   793   843 0.18211765 0.20866132 0.13405761 0.23017769
 [15,]   844   888 0.14957778 0.17634812 0.10633717 0.19281839
 [16,]   889   938 0.19734000 0.19177406 0.15273001 0.24194999
 [17,]   939   943 0.86680000 0.05580502 0.82574978 0.90785022
 [18,]   944  1018 0.17470667 0.10118618 0.15548824 0.19392509
 [19,]  1019  1032 0.58428571 0.34496364 0.43263776 0.73593367
 [20,]  1033  1142 0.18816364 0.08463429 0.17489039 0.20143689
 [21,]  1143  1176 0.18558824 0.15582024 0.14163289 0.22954358
 [22,]  1177  1178 1.59500000 0.60952605 0.88606809 2.30393191
 [23,]  1179  1368 0.32744737 0.28211650 0.29378234 0.36111239
 [24,]  1369  1375 0.95057143 0.51726327 0.62899080 1.27215205
 [25,]  1376  1466 0.21749451 0.25720279 0.17314567 0.26184334
 [26,]  1467  1560 0.21709574 0.22015376 0.17974584 0.25444564
 [27,]  1561  1605 0.30462222 0.40168567 0.20612878 0.40311567
 [28,]  1606  1714 0.17048624 0.14168952 0.14816328 0.19280920
 [29,]  1715  1955 0.14351452 0.11244080 0.13160093 0.15542811
 [30,]  1956  1959 0.84625000 0.16573950 0.70994139 0.98255861
 [31,]  1960  2067 0.18087037 0.09166977 0.16636123 0.19537951
 [32,]  2068  2144 0.19009091 0.11276725 0.16895286 0.21122896
 [33,]  2145  2147 1.92800000 0.91683314 1.05732328 2.79867672
 [34,]  2148  2232 0.44197647 0.43827136 0.36378473 0.52016821
 [35,]  2233  2252 0.79275000 0.43621348 0.63231049 0.95318951
 [36,]  2253  2328 0.15798684 0.11320336 0.13662790 0.17934579
 [37,]  2329  2335 0.90457143 0.42456050 0.64062376 1.16851909
 [38,]  2336  2472 0.20097810 0.18653806 0.17476403 0.22719217
 [39,]  2473  2659 0.17288235 0.10114196 0.16071663 0.18504807
 [40,]  2660  2670 0.43463636 0.27245266 0.29951564 0.56975709
 [41,]  2671  2672 0.33850000 0.18031223 0.12878116 0.54821884
 [42,]  2673  2676 0.37275000 0.11853375 0.27526467 0.47023533
 [43,]  2677  3132 0.27140132 0.12122497 0.26206368 0.28073895
 [44,]  3133  3414 0.24971277 0.10382509 0.23954314 0.25988239
 [45,]  3415  3485 0.26271831 0.08814455 0.24551177 0.27992485
 [46,]  3486  3548 0.78198413 0.43995383 0.69081158 0.87315667
 [47,]  3549  3634 0.51816279 0.27488166 0.46940727 0.56691831
 [48,]  3635  3718 0.33130952 0.13270603 0.30749301 0.35512604
 [49,]  3719  3721 0.17733333 0.04707795 0.13262544 0.22204122
 [50,]  3722  4070 0.15686819 0.10889972 0.14727990 0.16645649
 [51,]  4071  4077 0.79457143 0.04656485 0.76562223 0.82352062
 [52,]  4078  4292 0.15275349 0.10925063 0.14049796 0.16500902
 [53,]  4293  4445 0.38272549 0.31320973 0.34107532 0.42437566
 [54,]  4446  4544 0.58247475 0.29179862 0.53423635 0.63071315
 [55,]  4545  4548 1.27175000 0.09571268 1.19303333 1.35046667
 [56,]  4549  4863 0.39110159 0.18810262 0.37366880 0.40853437
 [57,]  4864  4963 0.47486000 0.26307283 0.43158837 0.51813163
 [58,]  4964  4980 1.02270588 0.49126759 0.82672175 1.21869001
 [59,]  4981  5146 0.35089759 0.13298570 0.33391992 0.36787526
 [60,]  5147  5636 0.41125306 0.23248036 0.39397816 0.42852796
 [61,]  5637  5718 0.44039024 0.22202345 0.40006108 0.48071940
 [62,]  5719  6011 0.49818430 0.28837213 0.47047365 0.52589495
 [63,]  6012  6017 1.02733333 0.18042912 0.90617361 1.14849305
 [64,]  6018  6230 0.42732864 0.23357544 0.40100387 0.45365341
 [65,]  6231  6235 1.04000000 0.11135529 0.95808695 1.12191305
 [66,]  6236  6395 0.30019375 0.11013665 0.28587190 0.31451560
 [67,]  6396  6558 0.40439877 0.22889281 0.37490939 0.43388815
 [68,]  6559  6565 0.82328571 0.09350528 0.76515383 0.88141760
 [69,]  6566  6679 0.27366667 0.05711093 0.26486846 0.28246487
 [70,]  6680  6683 0.85625000 0.06570832 0.80220972 0.91029028
 [71,]  6684  6803 0.28623333 0.12169632 0.26796015 0.30450651
 [72,]  6804  7146 0.52422449 0.29807168 0.49775163 0.55069735
 [73,]  7147  7350 0.31261275 0.26878429 0.28165880 0.34356669
 [74,]  7351  7671 0.42755452 0.29384902 0.40057719 0.45453185
 [75,]  7672  8109 0.40544749 0.32093285 0.38022405 0.43067093
 [76,]  8110  8312 0.38780788 0.32644096 0.35012155 0.42549422
 [77,]  8313  8516 0.40003922 0.33977798 0.36090944 0.43916899
 [78,]  8517  8893 0.37877454 0.28384803 0.35472856 0.40282051
 [79,]  8894  9536 0.44813997 0.32546790 0.42702794 0.46925200
 [80,]  9537  9765 0.65152402 0.32387550 0.61632037 0.68672766
 [81,]  9766 10068 0.49356106 0.25400972 0.46955857 0.51756354
 [82,] 10069 10134 0.55030303 0.34243544 0.48097099 0.61963507
 [83,] 10135 10140 1.35683333 0.22070017 1.20863125 1.50503542
 [84,] 10141 10236 0.36204167 0.16197329 0.33485005 0.38923328
 [85,] 10237 10332 0.43502083 0.26903739 0.38985560 0.48018607
 [86,] 10333 10437 0.35643810 0.17702993 0.32802103 0.38485517
 [87,] 10438 10517 0.37961250 0.18511826 0.34556921 0.41365579
 [88,] 10518 10591 0.55533784 0.37381261 0.48386100 0.62681468
 [89,] 10592 10928 0.59558457 0.32778050 0.56621515 0.62495399
 [90,] 10929 10937 1.56755556 0.25295015 1.42886690 1.70624421
 [91,] 10938 11783 0.52851655 0.30350088 0.51135320 0.54567990
 [92,] 11784 11969 0.49110753 0.26559050 0.45907558 0.52313947
 [93,] 11970 12056 0.39251724 0.17475344 0.36170003 0.42333445
 [94,] 12057 12131 0.45756000 0.25458680 0.40920599 0.50591401
 [95,] 12132 12726 0.51640672 0.26338135 0.49864628 0.53416717
 [96,] 12727 12732 1.29900000 0.20395686 1.16204120 1.43595880
 [97,] 12733 13382 0.51832462 0.27554900 0.50054717 0.53610206
 [98,] 13383 13603 0.43812670 0.24908223 0.41056704 0.46568636
 [99,] 13604 13870 0.43283521 0.20554365 0.41214448 0.45352593
[100,] 13871 14255 0.40451948 0.21354805 0.38661785 0.42242111
[101,] 14256 14551 0.29983784 0.21176357 0.27959214 0.32008353
[102,] 14552 14650 0.32707071 0.28589020 0.27980905 0.37433236
[103,] 14651 14656 0.99883333 0.15830656 0.89252910 1.10513756
[104,] 14657 14964 0.31847727 0.12287769 0.30696065 0.32999389
[105,] 14965 15052 0.38471591 0.16570303 0.35566121 0.41377061
[106,] 15053 15059 1.37171429 0.11153731 1.30237196 1.44105661
[107,] 15060 15128 0.41749275 0.21528795 0.37486206 0.46012345
[108,] 15129 15315 0.39243316 0.20651343 0.36759297 0.41727334
[109,] 15316 15342 0.62296296 0.27143245 0.53704042 0.70888551
[110,] 15343 15376 0.38794118 0.13503713 0.34984854 0.42603381
[111,] 15377 15382 0.31166667 0.08084965 0.25737542 0.36595791
[112,] 15383 15386 0.28250000 0.07889867 0.21761162 0.34738838
[113,] 15387 15389 0.29366667 0.08485478 0.21308376 0.37424957
[114,] 15390 15400 0.27827273 0.04756909 0.25468122 0.30186424
[115,] 15401 15832 0.29526157 0.07287639 0.28949428 0.30102887
[116,] 15833 15838 0.94333333 0.04131182 0.91559209 0.97107458
[117,] 15839 16069 0.31194805 0.08370563 0.30288914 0.32100696
[118,] 16070 16280 0.32904265 0.16637795 0.31020262 0.34788269
[119,] 16281 16307 0.41777778 0.29539365 0.32427026 0.51128530
[120,] 16308 16496 0.30491005 0.06815562 0.29675554 0.31306457
[121,] 16497 16539 0.63139535 0.53210535 0.49792320 0.76486750
[122,] 16540 16602 0.51480952 0.28441444 0.45586973 0.57374932
[123,] 16603 16612 0.36000000 0.01632993 0.35150601 0.36849399
[124,] 16613 16663 0.34460784 0.15144307 0.30972663 0.37948906
[125,] 16664 17012 0.30289112 0.08392813 0.29550150 0.31028074
[126,] 17013 17046 0.60958824 0.29497931 0.52637750 0.69279897
[127,] 17047 17301 0.31431765 0.06981058 0.30712683 0.32150847
[128,] 17302 17309 0.62087500 0.30015779 0.44632017 0.79542983
[129,] 17310 17404 0.29954737 0.07243482 0.28732338 0.31177135
[130,] 17405 17429 0.47876000 0.31626707 0.37471739 0.58280261
[131,] 17430 17650 0.30241176 0.07319617 0.29431299 0.31051054
[132,] 17651 17658 1.01375000 0.02825269 0.99731983 1.03018017
[133,] 17659 17908 0.32274000 0.08432301 0.31396790 0.33151210
[134,] 17909 18014 0.36847170 0.12502687 0.34849712 0.38844628
[135,] 18015 18072 0.45996552 0.22579233 0.41119892 0.50873211
[136,] 18073 18074 1.84500000 0.60104076 1.14593721 2.54406279
[137,] 18075 18173 0.49686869 0.27816552 0.45088403 0.54285334
[138,] 18174 18264 0.32750549 0.08028289 0.31366252 0.34134847
[139,] 18265 18360 0.34258333 0.19949234 0.30909312 0.37607354
[140,] 18361 18433 0.38735616 0.20038306 0.34877931 0.42593302
[141,] 18434 18437 1.30000000 0.06377042 1.24755350 1.35244650
[142,] 18438 18608 0.32561404 0.07849521 0.31574051 0.33548756
[143,] 18609 18615 1.01857143 0.12992672 0.93779648 1.09934638
[144,] 18616 18788 0.30564740 0.07546235 0.29621039 0.31508441
[145,] 18789 18899 0.30182883 0.05804275 0.29276704 0.31089061
[146,] 18900 18905 1.08166667 0.04070217 1.05433481 1.10899853
[147,] 18906 19048 0.34625874 0.09462407 0.33324324 0.35927424
[148,] 19049 19050 1.60950000 0.71771338 0.77473678 2.44426322
[149,] 19051 19261 0.32608531 0.10333138 0.31438444 0.33778618
[150,] 19262 19267 0.92983333 0.05562523 0.89248051 0.96718616
[151,] 19268 19519 0.30967063 0.07631779 0.30176289 0.31757838
[152,] 19520 19624 0.29762857 0.08261440 0.28436720 0.31088994
[153,] 19625 19629 0.96340000 0.11106440 0.88170093 1.04509907
[154,] 19630 19845 0.29996296 0.08392853 0.29056984 0.30935609
[155,] 19846 19852 0.88614286 0.10043311 0.82370397 0.94858174
[156,] 19853 20015 0.28745399 0.07412420 0.27790420 0.29700377
[157,] 20016 20017 1.65850000 0.26657926 1.34844509 1.96855491
[158,] 20018 20092 0.42212000 0.17762259 0.38838390 0.45585610
[159,] 20093 20103 0.87218182 0.47699556 0.63561967 1.10874396
[160,] 20104 20296 0.30757513 0.08093623 0.29799235 0.31715791
[161,] 20297 20340 0.60940909 0.39307880 0.51193694 0.70688124
[162,] 20341 20478 0.36581159 0.20908988 0.33653499 0.39508820
[163,] 20479 20569 0.44713187 0.15490586 0.42042184 0.47384190
[164,] 20570 20578 2.04088889 0.63784823 1.69116657 2.39061121
[165,] 20579 20720 0.57975352 0.26651694 0.54296538 0.61654167
[166,] 20721 20726 1.20166667 0.18071156 1.08031729 1.32301605
 [ reached getOption("max.print") -- omitted 788 rows ]

plot(summary(bcp))

[1] "Change points"

[1] "Segments"
       begin   end       mean         SD   LL of CI   UL of CI
  [1,]     1    68 0.11851471 0.05251481 0.10803969 0.12898972
  [2,]    69    89 0.70085714 0.55165300 0.50284875 0.89886553
  [3,]    90   233 0.14222917 0.09243684 0.12955874 0.15489959
  [4,]   234   240 0.91714286 0.55476224 0.57224926 1.26203645
  [5,]   241   408 0.37915476 0.27470555 0.34429375 0.41401578
  [6,]   409   410 1.73000000 0.66468037 0.95691880 2.50308120
  [7,]   411   524 0.45407895 0.32414913 0.40414226 0.50401563
  [8,]   525   526 1.84250000 0.59609102 1.14919420 2.53580580
  [9,]   527   599 0.17747945 0.06771880 0.16444253 0.19051638
 [10,]   600   602 1.19966667 0.60550007 0.62464948 1.77468385
 [11,]   603   696 0.22370213 0.15978499 0.19659401 0.25081024
 [12,]   697   724 0.26989286 0.26216920 0.18839805 0.35138766
 [13,]   725   792 0.21539706 0.23737175 0.16804904 0.26274508
 [14,]   793   843 0.18211765 0.20866132 0.13405761 0.23017769
 [15,]   844   888 0.14957778 0.17634812 0.10633717 0.19281839
 [16,]   889   938 0.19734000 0.19177406 0.15273001 0.24194999
 [17,]   939   943 0.86680000 0.05580502 0.82574978 0.90785022
 [18,]   944  1018 0.17470667 0.10118618 0.15548824 0.19392509
 [19,]  1019  1032 0.58428571 0.34496364 0.43263776 0.73593367
 [20,]  1033  1142 0.18816364 0.08463429 0.17489039 0.20143689
 [21,]  1143  1176 0.18558824 0.15582024 0.14163289 0.22954358
 [22,]  1177  1178 1.59500000 0.60952605 0.88606809 2.30393191
 [23,]  1179  1368 0.32744737 0.28211650 0.29378234 0.36111239
 [24,]  1369  1375 0.95057143 0.51726327 0.62899080 1.27215205
 [25,]  1376  1466 0.21749451 0.25720279 0.17314567 0.26184334
 [26,]  1467  1560 0.21709574 0.22015376 0.17974584 0.25444564
 [27,]  1561  1605 0.30462222 0.40168567 0.20612878 0.40311567
 [28,]  1606  1714 0.17048624 0.14168952 0.14816328 0.19280920
 [29,]  1715  1955 0.14351452 0.11244080 0.13160093 0.15542811
 [30,]  1956  1959 0.84625000 0.16573950 0.70994139 0.98255861
 [31,]  1960  2067 0.18087037 0.09166977 0.16636123 0.19537951
 [32,]  2068  2144 0.19009091 0.11276725 0.16895286 0.21122896
 [33,]  2145  2147 1.92800000 0.91683314 1.05732328 2.79867672
 [34,]  2148  2232 0.44197647 0.43827136 0.36378473 0.52016821
 [35,]  2233  2252 0.79275000 0.43621348 0.63231049 0.95318951
 [36,]  2253  2328 0.15798684 0.11320336 0.13662790 0.17934579
 [37,]  2329  2335 0.90457143 0.42456050 0.64062376 1.16851909
 [38,]  2336  2472 0.20097810 0.18653806 0.17476403 0.22719217
 [39,]  2473  2659 0.17288235 0.10114196 0.16071663 0.18504807
 [40,]  2660  2670 0.43463636 0.27245266 0.29951564 0.56975709
 [41,]  2671  2672 0.33850000 0.18031223 0.12878116 0.54821884
 [42,]  2673  2676 0.37275000 0.11853375 0.27526467 0.47023533
 [43,]  2677  3132 0.27140132 0.12122497 0.26206368 0.28073895
 [44,]  3133  3414 0.24971277 0.10382509 0.23954314 0.25988239
 [45,]  3415  3485 0.26271831 0.08814455 0.24551177 0.27992485
 [46,]  3486  3548 0.78198413 0.43995383 0.69081158 0.87315667
 [47,]  3549  3634 0.51816279 0.27488166 0.46940727 0.56691831
 [48,]  3635  3718 0.33130952 0.13270603 0.30749301 0.35512604
 [49,]  3719  3721 0.17733333 0.04707795 0.13262544 0.22204122
 [50,]  3722  4070 0.15686819 0.10889972 0.14727990 0.16645649
 [51,]  4071  4077 0.79457143 0.04656485 0.76562223 0.82352062
 [52,]  4078  4292 0.15275349 0.10925063 0.14049796 0.16500902
 [53,]  4293  4445 0.38272549 0.31320973 0.34107532 0.42437566
 [54,]  4446  4544 0.58247475 0.29179862 0.53423635 0.63071315
 [55,]  4545  4548 1.27175000 0.09571268 1.19303333 1.35046667
 [56,]  4549  4863 0.39110159 0.18810262 0.37366880 0.40853437
 [57,]  4864  4963 0.47486000 0.26307283 0.43158837 0.51813163
 [58,]  4964  4980 1.02270588 0.49126759 0.82672175 1.21869001
 [59,]  4981  5146 0.35089759 0.13298570 0.33391992 0.36787526
 [60,]  5147  5636 0.41125306 0.23248036 0.39397816 0.42852796
 [61,]  5637  5718 0.44039024 0.22202345 0.40006108 0.48071940
 [62,]  5719  6011 0.49818430 0.28837213 0.47047365 0.52589495
 [63,]  6012  6017 1.02733333 0.18042912 0.90617361 1.14849305
 [64,]  6018  6230 0.42732864 0.23357544 0.40100387 0.45365341
 [65,]  6231  6235 1.04000000 0.11135529 0.95808695 1.12191305
 [66,]  6236  6395 0.30019375 0.11013665 0.28587190 0.31451560
 [67,]  6396  6558 0.40439877 0.22889281 0.37490939 0.43388815
 [68,]  6559  6565 0.82328571 0.09350528 0.76515383 0.88141760
 [69,]  6566  6679 0.27366667 0.05711093 0.26486846 0.28246487
 [70,]  6680  6683 0.85625000 0.06570832 0.80220972 0.91029028
 [71,]  6684  6803 0.28623333 0.12169632 0.26796015 0.30450651
 [72,]  6804  7146 0.52422449 0.29807168 0.49775163 0.55069735
 [73,]  7147  7350 0.31261275 0.26878429 0.28165880 0.34356669
 [74,]  7351  7671 0.42755452 0.29384902 0.40057719 0.45453185
 [75,]  7672  8109 0.40544749 0.32093285 0.38022405 0.43067093
 [76,]  8110  8312 0.38780788 0.32644096 0.35012155 0.42549422
 [77,]  8313  8516 0.40003922 0.33977798 0.36090944 0.43916899
 [78,]  8517  8893 0.37877454 0.28384803 0.35472856 0.40282051
 [79,]  8894  9536 0.44813997 0.32546790 0.42702794 0.46925200
 [80,]  9537  9765 0.65152402 0.32387550 0.61632037 0.68672766
 [81,]  9766 10068 0.49356106 0.25400972 0.46955857 0.51756354
 [82,] 10069 10134 0.55030303 0.34243544 0.48097099 0.61963507
 [83,] 10135 10140 1.35683333 0.22070017 1.20863125 1.50503542
 [84,] 10141 10236 0.36204167 0.16197329 0.33485005 0.38923328
 [85,] 10237 10332 0.43502083 0.26903739 0.38985560 0.48018607
 [86,] 10333 10437 0.35643810 0.17702993 0.32802103 0.38485517
 [87,] 10438 10517 0.37961250 0.18511826 0.34556921 0.41365579
 [88,] 10518 10591 0.55533784 0.37381261 0.48386100 0.62681468
 [89,] 10592 10928 0.59558457 0.32778050 0.56621515 0.62495399
 [90,] 10929 10937 1.56755556 0.25295015 1.42886690 1.70624421
 [91,] 10938 11783 0.52851655 0.30350088 0.51135320 0.54567990
 [92,] 11784 11969 0.49110753 0.26559050 0.45907558 0.52313947
 [93,] 11970 12056 0.39251724 0.17475344 0.36170003 0.42333445
 [94,] 12057 12131 0.45756000 0.25458680 0.40920599 0.50591401
 [95,] 12132 12726 0.51640672 0.26338135 0.49864628 0.53416717
 [96,] 12727 12732 1.29900000 0.20395686 1.16204120 1.43595880
 [97,] 12733 13382 0.51832462 0.27554900 0.50054717 0.53610206
 [98,] 13383 13603 0.43812670 0.24908223 0.41056704 0.46568636
 [99,] 13604 13870 0.43283521 0.20554365 0.41214448 0.45352593
[100,] 13871 14255 0.40451948 0.21354805 0.38661785 0.42242111
[101,] 14256 14551 0.29983784 0.21176357 0.27959214 0.32008353
[102,] 14552 14650 0.32707071 0.28589020 0.27980905 0.37433236
[103,] 14651 14656 0.99883333 0.15830656 0.89252910 1.10513756
[104,] 14657 14964 0.31847727 0.12287769 0.30696065 0.32999389
[105,] 14965 15052 0.38471591 0.16570303 0.35566121 0.41377061
[106,] 15053 15059 1.37171429 0.11153731 1.30237196 1.44105661
[107,] 15060 15128 0.41749275 0.21528795 0.37486206 0.46012345
[108,] 15129 15315 0.39243316 0.20651343 0.36759297 0.41727334
[109,] 15316 15342 0.62296296 0.27143245 0.53704042 0.70888551
[110,] 15343 15376 0.38794118 0.13503713 0.34984854 0.42603381
[111,] 15377 15382 0.31166667 0.08084965 0.25737542 0.36595791
[112,] 15383 15386 0.28250000 0.07889867 0.21761162 0.34738838
[113,] 15387 15389 0.29366667 0.08485478 0.21308376 0.37424957
[114,] 15390 15400 0.27827273 0.04756909 0.25468122 0.30186424
[115,] 15401 15832 0.29526157 0.07287639 0.28949428 0.30102887
[116,] 15833 15838 0.94333333 0.04131182 0.91559209 0.97107458
[117,] 15839 16069 0.31194805 0.08370563 0.30288914 0.32100696
[118,] 16070 16280 0.32904265 0.16637795 0.31020262 0.34788269
[119,] 16281 16307 0.41777778 0.29539365 0.32427026 0.51128530
[120,] 16308 16496 0.30491005 0.06815562 0.29675554 0.31306457
[121,] 16497 16539 0.63139535 0.53210535 0.49792320 0.76486750
[122,] 16540 16602 0.51480952 0.28441444 0.45586973 0.57374932
[123,] 16603 16612 0.36000000 0.01632993 0.35150601 0.36849399
[124,] 16613 16663 0.34460784 0.15144307 0.30972663 0.37948906
[125,] 16664 17012 0.30289112 0.08392813 0.29550150 0.31028074
[126,] 17013 17046 0.60958824 0.29497931 0.52637750 0.69279897
[127,] 17047 17301 0.31431765 0.06981058 0.30712683 0.32150847
[128,] 17302 17309 0.62087500 0.30015779 0.44632017 0.79542983
[129,] 17310 17404 0.29954737 0.07243482 0.28732338 0.31177135
[130,] 17405 17429 0.47876000 0.31626707 0.37471739 0.58280261
[131,] 17430 17650 0.30241176 0.07319617 0.29431299 0.31051054
[132,] 17651 17658 1.01375000 0.02825269 0.99731983 1.03018017
[133,] 17659 17908 0.32274000 0.08432301 0.31396790 0.33151210
[134,] 17909 18014 0.36847170 0.12502687 0.34849712 0.38844628
[135,] 18015 18072 0.45996552 0.22579233 0.41119892 0.50873211
[136,] 18073 18074 1.84500000 0.60104076 1.14593721 2.54406279
[137,] 18075 18173 0.49686869 0.27816552 0.45088403 0.54285334
[138,] 18174 18264 0.32750549 0.08028289 0.31366252 0.34134847
[139,] 18265 18360 0.34258333 0.19949234 0.30909312 0.37607354
[140,] 18361 18433 0.38735616 0.20038306 0.34877931 0.42593302
[141,] 18434 18437 1.30000000 0.06377042 1.24755350 1.35244650
[142,] 18438 18608 0.32561404 0.07849521 0.31574051 0.33548756
[143,] 18609 18615 1.01857143 0.12992672 0.93779648 1.09934638
[144,] 18616 18788 0.30564740 0.07546235 0.29621039 0.31508441
[145,] 18789 18899 0.30182883 0.05804275 0.29276704 0.31089061
[146,] 18900 18905 1.08166667 0.04070217 1.05433481 1.10899853
[147,] 18906 19048 0.34625874 0.09462407 0.33324324 0.35927424
[148,] 19049 19050 1.60950000 0.71771338 0.77473678 2.44426322
[149,] 19051 19261 0.32608531 0.10333138 0.31438444 0.33778618
[150,] 19262 19267 0.92983333 0.05562523 0.89248051 0.96718616
[151,] 19268 19519 0.30967063 0.07631779 0.30176289 0.31757838
[152,] 19520 19624 0.29762857 0.08261440 0.28436720 0.31088994
[153,] 19625 19629 0.96340000 0.11106440 0.88170093 1.04509907
[154,] 19630 19845 0.29996296 0.08392853 0.29056984 0.30935609
[155,] 19846 19852 0.88614286 0.10043311 0.82370397 0.94858174
[156,] 19853 20015 0.28745399 0.07412420 0.27790420 0.29700377
[157,] 20016 20017 1.65850000 0.26657926 1.34844509 1.96855491
[158,] 20018 20092 0.42212000 0.17762259 0.38838390 0.45585610
[159,] 20093 20103 0.87218182 0.47699556 0.63561967 1.10874396
[160,] 20104 20296 0.30757513 0.08093623 0.29799235 0.31715791
[161,] 20297 20340 0.60940909 0.39307880 0.51193694 0.70688124
[162,] 20341 20478 0.36581159 0.20908988 0.33653499 0.39508820
[163,] 20479 20569 0.44713187 0.15490586 0.42042184 0.47384190
[164,] 20570 20578 2.04088889 0.63784823 1.69116657 2.39061121
[165,] 20579 20720 0.57975352 0.26651694 0.54296538 0.61654167
[166,] 20721 20726 1.20166667 0.18071156 1.08031729 1.32301605
 [ reached getOption("max.print") -- omitted 788 rows ]

Nice! Looks like it still works over a longer period. (An advantage of an onlineBcp model?)

It looks like any interval with a mean above about 2.0 kWh should qualify as an EV charging session. - Do we need to do anything to avoid mixups with high HVAC usage? - Do we need to put an ideal length of charge on the intervals? Maximum variation during the period? - Use post.prob to filter out any intervals?

result <- summary(bcp, norm.test = TRUE)

Warning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov 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should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov test

[1] "Change points"

[1] "Segments"
       begin   end       mean         SD   LL of CI   UL of CI normality p-value
  [1,]     1    68 0.11851471 0.05251481 0.10803969 0.12898972      5.558635e-02
  [2,]    69    89 0.70085714 0.55165300 0.50284875 0.89886553      2.356642e-01
  [3,]    90   233 0.14222917 0.09243684 0.12955874 0.15489959      5.017888e-02
  [4,]   234   240 0.91714286 0.55476224 0.57224926 1.26203645      8.598808e-01
  [5,]   241   408 0.37915476 0.27470555 0.34429375 0.41401578      2.983556e-05
  [6,]   409   410 1.73000000 0.66468037 0.95691880 2.50308120      9.991595e-01
  [7,]   411   524 0.45407895 0.32414913 0.40414226 0.50401563      1.754392e-03
  [8,]   525   526 1.84250000 0.59609102 1.14919420 2.53580580      9.991595e-01
  [9,]   527   599 0.17747945 0.06771880 0.16444253 0.19051638      2.609959e-02
 [10,]   600   602 1.19966667 0.60550007 0.62464948 1.77468385      9.460188e-01
 [11,]   603   696 0.22370213 0.15978499 0.19659401 0.25081024      2.906650e-04
 [12,]   697   724 0.26989286 0.26216920 0.18839805 0.35138766      1.914599e-02
 [13,]   725   792 0.21539706 0.23737175 0.16804904 0.26274508      1.569400e-04
 [14,]   793   843 0.18211765 0.20866132 0.13405761 0.23017769      3.147528e-03
 [15,]   844   888 0.14957778 0.17634812 0.10633717 0.19281839      4.439415e-04
 [16,]   889   938 0.19734000 0.19177406 0.15273001 0.24194999      5.771098e-05
 [17,]   939   943 0.86680000 0.05580502 0.82574978 0.90785022      5.927433e-01
 [18,]   944  1018 0.17470667 0.10118618 0.15548824 0.19392509      1.033167e-01
 [19,]  1019  1032 0.58428571 0.34496364 0.43263776 0.73593367      2.792816e-01
 [20,]  1033  1142 0.18816364 0.08463429 0.17489039 0.20143689      4.438916e-03
 [21,]  1143  1176 0.18558824 0.15582024 0.14163289 0.22954358      1.927116e-02
 [22,]  1177  1178 1.59500000 0.60952605 0.88606809 2.30393191      9.991595e-01
 [23,]  1179  1368 0.32744737 0.28211650 0.29378234 0.36111239      1.199396e-07
 [24,]  1369  1375 0.95057143 0.51726327 0.62899080 1.27215205      3.258514e-01
 [25,]  1376  1466 0.21749451 0.25720279 0.17314567 0.26184334      3.567013e-11
 [26,]  1467  1560 0.21709574 0.22015376 0.17974584 0.25444564      6.201917e-06
 [27,]  1561  1605 0.30462222 0.40168567 0.20612878 0.40311567      1.684396e-03
 [28,]  1606  1714 0.17048624 0.14168952 0.14816328 0.19280920      6.067085e-04
 [29,]  1715  1955 0.14351452 0.11244080 0.13160093 0.15542811      1.000881e-06
 [30,]  1956  1959 0.84625000 0.16573950 0.70994139 0.98255861      7.932280e-01
 [31,]  1960  2067 0.18087037 0.09166977 0.16636123 0.19537951      1.412459e-01
 [32,]  2068  2144 0.19009091 0.11276725 0.16895286 0.21122896      1.848765e-01
 [33,]  2145  2147 1.92800000 0.91683314 1.05732328 2.79867672      7.289916e-01
 [34,]  2148  2232 0.44197647 0.43827136 0.36378473 0.52016821      1.045416e-03
 [35,]  2233  2252 0.79275000 0.43621348 0.63231049 0.95318951      9.509594e-01
 [36,]  2253  2328 0.15798684 0.11320336 0.13662790 0.17934579      3.341529e-03
 [37,]  2329  2335 0.90457143 0.42456050 0.64062376 1.16851909      7.828116e-01
 [38,]  2336  2472 0.20097810 0.18653806 0.17476403 0.22719217      3.578956e-08
 [39,]  2473  2659 0.17288235 0.10114196 0.16071663 0.18504807      4.748881e-04
 [40,]  2660  2670 0.43463636 0.27245266 0.29951564 0.56975709      9.083194e-01
 [41,]  2671  2672 0.33850000 0.18031223 0.12878116 0.54821884      9.991595e-01
 [42,]  2673  2676 0.37275000 0.11853375 0.27526467 0.47023533      9.750718e-01
 [43,]  2677  3132 0.27140132 0.12122497 0.26206368 0.28073895      5.356035e-09
 [44,]  3133  3414 0.24971277 0.10382509 0.23954314 0.25988239      8.637535e-14
 [45,]  3415  3485 0.26271831 0.08814455 0.24551177 0.27992485      9.550424e-07
 [46,]  3486  3548 0.78198413 0.43995383 0.69081158 0.87315667      1.218112e-01
 [47,]  3549  3634 0.51816279 0.27488166 0.46940727 0.56691831      2.263517e-02
 [48,]  3635  3718 0.33130952 0.13270603 0.30749301 0.35512604      1.468338e-02
 [49,]  3719  3721 0.17733333 0.04707795 0.13262544 0.22204122      8.058098e-01
 [50,]  3722  4070 0.15686819 0.10889972 0.14727990 0.16645649      1.034916e-05
 [51,]  4071  4077 0.79457143 0.04656485 0.76562223 0.82352062      9.627227e-01
 [52,]  4078  4292 0.15275349 0.10925063 0.14049796 0.16500902      1.564990e-02
 [53,]  4293  4445 0.38272549 0.31320973 0.34107532 0.42437566      8.694972e-03
 [54,]  4446  4544 0.58247475 0.29179862 0.53423635 0.63071315      1.060064e-02
 [55,]  4545  4548 1.27175000 0.09571268 1.19303333 1.35046667      8.235734e-01
 [56,]  4549  4863 0.39110159 0.18810262 0.37366880 0.40853437      1.393781e-05
 [57,]  4864  4963 0.47486000 0.26307283 0.43158837 0.51813163      6.418883e-04
 [58,]  4964  4980 1.02270588 0.49126759 0.82672175 1.21869001      9.594757e-01
 [59,]  4981  5146 0.35089759 0.13298570 0.33391992 0.36787526      4.708571e-05
 [60,]  5147  5636 0.41125306 0.23248036 0.39397816 0.42852796      1.728617e-13
 [61,]  5637  5718 0.44039024 0.22202345 0.40006108 0.48071940      1.462427e-02
 [62,]  5719  6011 0.49818430 0.28837213 0.47047365 0.52589495      4.459741e-04
 [63,]  6012  6017 1.02733333 0.18042912 0.90617361 1.14849305      6.175601e-01
 [64,]  6018  6230 0.42732864 0.23357544 0.40100387 0.45365341      6.726460e-03
 [65,]  6231  6235 1.04000000 0.11135529 0.95808695 1.12191305      9.340271e-01
 [66,]  6236  6395 0.30019375 0.11013665 0.28587190 0.31451560      7.056873e-06
 [67,]  6396  6558 0.40439877 0.22889281 0.37490939 0.43388815      2.036173e-03
 [68,]  6559  6565 0.82328571 0.09350528 0.76515383 0.88141760      3.660591e-01
 [69,]  6566  6679 0.27366667 0.05711093 0.26486846 0.28246487      6.685227e-02
 [70,]  6680  6683 0.85625000 0.06570832 0.80220972 0.91029028      7.615567e-01
 [71,]  6684  6803 0.28623333 0.12169632 0.26796015 0.30450651      1.462045e-03
 [72,]  6804  7146 0.52422449 0.29807168 0.49775163 0.55069735      1.414514e-05
 [73,]  7147  7350 0.31261275 0.26878429 0.28165880 0.34356669      1.471900e-05
 [74,]  7351  7671 0.42755452 0.29384902 0.40057719 0.45453185      4.247697e-02
 [75,]  7672  8109 0.40544749 0.32093285 0.38022405 0.43067093      1.267526e-04
 [76,]  8110  8312 0.38780788 0.32644096 0.35012155 0.42549422      4.003263e-03
 [77,]  8313  8516 0.40003922 0.33977798 0.36090944 0.43916899      3.147864e-03
 [78,]  8517  8893 0.37877454 0.28384803 0.35472856 0.40282051      9.653318e-04
 [79,]  8894  9536 0.44813997 0.32546790 0.42702794 0.46925200      5.326316e-05
 [80,]  9537  9765 0.65152402 0.32387550 0.61632037 0.68672766      7.852331e-03
 [81,]  9766 10068 0.49356106 0.25400972 0.46955857 0.51756354      1.900428e-02
 [82,] 10069 10134 0.55030303 0.34243544 0.48097099 0.61963507      6.081360e-02
 [83,] 10135 10140 1.35683333 0.22070017 1.20863125 1.50503542      4.721032e-01
 [84,] 10141 10236 0.36204167 0.16197329 0.33485005 0.38923328      4.720172e-03
 [85,] 10237 10332 0.43502083 0.26903739 0.38985560 0.48018607      3.120705e-02
 [86,] 10333 10437 0.35643810 0.17702993 0.32802103 0.38485517      1.430184e-02
 [87,] 10438 10517 0.37961250 0.18511826 0.34556921 0.41365579      7.363339e-02
 [88,] 10518 10591 0.55533784 0.37381261 0.48386100 0.62681468      4.075243e-02
 [89,] 10592 10928 0.59558457 0.32778050 0.56621515 0.62495399      6.183850e-03
 [90,] 10929 10937 1.56755556 0.25295015 1.42886690 1.70624421      8.302274e-01
 [91,] 10938 11783 0.52851655 0.30350088 0.51135320 0.54567990      5.097511e-11
 [92,] 11784 11969 0.49110753 0.26559050 0.45907558 0.52313947      9.979034e-03
 [93,] 11970 12056 0.39251724 0.17475344 0.36170003 0.42333445      7.095404e-02
 [94,] 12057 12131 0.45756000 0.25458680 0.40920599 0.50591401      1.908937e-02
 [95,] 12132 12726 0.51640672 0.26338135 0.49864628 0.53416717      2.628987e-09
 [96,] 12727 12732 1.29900000 0.20395686 1.16204120 1.43595880      9.826903e-01
 [97,] 12733 13382 0.51832462 0.27554900 0.50054717 0.53610206      5.502915e-10
 [98,] 13383 13603 0.43812670 0.24908223 0.41056704 0.46568636      3.198705e-07
 [99,] 13604 13870 0.43283521 0.20554365 0.41214448 0.45352593      9.398370e-06
[100,] 13871 14255 0.40451948 0.21354805 0.38661785 0.42242111      4.476719e-11
[101,] 14256 14551 0.29983784 0.21176357 0.27959214 0.32008353      2.201160e-10
[102,] 14552 14650 0.32707071 0.28589020 0.27980905 0.37433236      8.394495e-05
[103,] 14651 14656 0.99883333 0.15830656 0.89252910 1.10513756      8.826387e-01
[104,] 14657 14964 0.31847727 0.12287769 0.30696065 0.32999389      2.299932e-02
[105,] 14965 15052 0.38471591 0.16570303 0.35566121 0.41377061      2.742575e-05
[106,] 15053 15059 1.37171429 0.11153731 1.30237196 1.44105661      8.768646e-01
[107,] 15060 15128 0.41749275 0.21528795 0.37486206 0.46012345      5.601728e-03
[108,] 15129 15315 0.39243316 0.20651343 0.36759297 0.41727334      6.875991e-06
[109,] 15316 15342 0.62296296 0.27143245 0.53704042 0.70888551      6.122554e-01
[110,] 15343 15376 0.38794118 0.13503713 0.34984854 0.42603381      5.095012e-01
[111,] 15377 15382 0.31166667 0.08084965 0.25737542 0.36595791      6.752876e-01
[112,] 15383 15386 0.28250000 0.07889867 0.21761162 0.34738838      5.126036e-01
[113,] 15387 15389 0.29366667 0.08485478 0.21308376 0.37424957      8.384185e-01
[114,] 15390 15400 0.27827273 0.04756909 0.25468122 0.30186424      2.677207e-01
[115,] 15401 15832 0.29526157 0.07287639 0.28949428 0.30102887      2.749247e-05
[116,] 15833 15838 0.94333333 0.04131182 0.91559209 0.97107458      9.928218e-01
[117,] 15839 16069 0.31194805 0.08370563 0.30288914 0.32100696      1.579487e-05
[118,] 16070 16280 0.32904265 0.16637795 0.31020262 0.34788269      2.123990e-11
[119,] 16281 16307 0.41777778 0.29539365 0.32427026 0.51128530      1.956821e-03
[120,] 16308 16496 0.30491005 0.06815562 0.29675554 0.31306457      3.143062e-02
[121,] 16497 16539 0.63139535 0.53210535 0.49792320 0.76486750      1.454945e-04
[122,] 16540 16602 0.51480952 0.28441444 0.45586973 0.57374932      1.797704e-03
[123,] 16603 16612 0.36000000 0.01632993 0.35150601 0.36849399      8.186212e-01
[124,] 16613 16663 0.34460784 0.15144307 0.30972663 0.37948906      1.685084e-03
[125,] 16664 17012 0.30289112 0.08392813 0.29550150 0.31028074      1.424762e-06
[126,] 17013 17046 0.60958824 0.29497931 0.52637750 0.69279897      2.099508e-01
[127,] 17047 17301 0.31431765 0.06981058 0.30712683 0.32150847      9.057676e-03
[128,] 17302 17309 0.62087500 0.30015779 0.44632017 0.79542983      8.517411e-01
[129,] 17310 17404 0.29954737 0.07243482 0.28732338 0.31177135      1.722243e-04
[130,] 17405 17429 0.47876000 0.31626707 0.37471739 0.58280261      3.673417e-03
[131,] 17430 17650 0.30241176 0.07319617 0.29431299 0.31051054      1.689783e-05
[132,] 17651 17658 1.01375000 0.02825269 0.99731983 1.03018017      9.393540e-01
[133,] 17659 17908 0.32274000 0.08432301 0.31396790 0.33151210      1.444155e-05
[134,] 17909 18014 0.36847170 0.12502687 0.34849712 0.38844628      3.577015e-02
[135,] 18015 18072 0.45996552 0.22579233 0.41119892 0.50873211      2.914935e-02
[136,] 18073 18074 1.84500000 0.60104076 1.14593721 2.54406279      9.991595e-01
[137,] 18075 18173 0.49686869 0.27816552 0.45088403 0.54285334      3.669993e-04
[138,] 18174 18264 0.32750549 0.08028289 0.31366252 0.34134847      2.043566e-02
[139,] 18265 18360 0.34258333 0.19949234 0.30909312 0.37607354      1.058334e-05
[140,] 18361 18433 0.38735616 0.20038306 0.34877931 0.42593302      2.253355e-04
[141,] 18434 18437 1.30000000 0.06377042 1.24755350 1.35244650      7.318359e-01
[142,] 18438 18608 0.32561404 0.07849521 0.31574051 0.33548756      2.529906e-03
 [ reached getOption("max.print") -- omitted 812 rows ]

bcp_segments <- as_tibble(result$result$segment) %>% 
  mutate(length = end - begin) %>% 
  mutate(lag_mean = lag(mean,1)) %>% 
  mutate(lead_mean = lead(mean,1)) %>% 
  mutate(adj = (lag_mean+lead_mean)/2)

bcp_segments %>% 
  filter(mean > 2.0) 

NA

sample_adj <-  sample %>%  
  left_join(bcp_segments, by = join_by(time_x>=begin,time_x<=end)) %>% 
  mutate(consumed_kWh_adj = case_when(
                                mean > 2.0 & mean < 3.0 ~ consumed_kWh - (mean - adj), 
                                .default = consumed_kWh
                                )
         )

sample_adj

NA
NA

Re-run on all three years, count charging sessions by month/year